<![CDATA[Heart disease is one of the leading causes of death worldwide, requiring an effective system to support early and objective diagnosis. This study aims to develop a web-based Decision Support System (DSS) to determine heart disease risk levels using the Fuzzy Mamdani method. The research methodology applies the Waterfall software development model, consisting of requirement analysis, system design, implementation, and testing stages. The system is developed using the Laravel framework and MySQL database, with input variables including age, blood pressure, cholesterol level, maximum heart rate, and ST depression processed through fuzzification, inference, and defuzzification. System testing was conducted using the Cleveland Heart Disease dataset to evaluate the reliability of the fuzzy inference results. The results indicate that the system is able to produce accurate and consistent heart disease risk levels in accordance with the defined fuzzy rules. Therefore, the proposed DSS can effectively support early heart disease diagnosis.]]>
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